Location
Grand Wailea, Hawaii
Event Website
https://hicss.hawaii.edu/
Start Date
7-1-2020 12:00 AM
End Date
10-1-2020 12:00 AM
Description
Rumor detection in recent years has emerged as an important research topic, as fake news on social media now has more significant impacts on people's lives, especially during complex and controversial events. Most existing rumor detection techniques, however, only provide shallow analyses of users who propagate rumors. In this paper, we propose a probabilistic model that describes user maliciousness with a two-sided perception of rumors and true stories. We model not only the behavior of retweeting rumors, but also the intention. We propose learning algorithms for discovering latent attributes and detecting rumors based on such attributes, supposedly more effectively when the stories involve retweets with mixed intentions. Using real-world rumor datasets, we show that our approach can outperform existing methods in detecting rumors, especially for more confusing stories. We also show that our approach can capture malicious users more effectively.
A Probabilistic Model for Malicious User and Rumor Detection on Social Media
Grand Wailea, Hawaii
Rumor detection in recent years has emerged as an important research topic, as fake news on social media now has more significant impacts on people's lives, especially during complex and controversial events. Most existing rumor detection techniques, however, only provide shallow analyses of users who propagate rumors. In this paper, we propose a probabilistic model that describes user maliciousness with a two-sided perception of rumors and true stories. We model not only the behavior of retweeting rumors, but also the intention. We propose learning algorithms for discovering latent attributes and detecting rumors based on such attributes, supposedly more effectively when the stories involve retweets with mixed intentions. Using real-world rumor datasets, we show that our approach can outperform existing methods in detecting rumors, especially for more confusing stories. We also show that our approach can capture malicious users more effectively.
https://aisel.aisnet.org/hicss-53/dsm/decision_making_in_osn/2